Demos
The SDK ships with several demo scripts in the demos/ directory. These are self-contained examples you can run directly to explore QTradeX features.
| Demo | File | Description |
|---|---|---|
| Extinction Event | demos/extinction_event.py |
A complete EMA crossover bot with multi-level support/resistance channels. Demonstrates BaseBot subclassing, tune/clamps, multi-indicator indicators(), trend-detect strategy(), and plot() with shaded zones. Runs standalone — uses qx.dispatch(). |
| Extinction Event v2 | demos/extinction_event_v2.py |
Architectural preview of the upcoming multi-asset engine (supports N assets). Uses qx.Allocation and qx.Limit signals, multi-asset Data(assets=[...]), and the new execution(allocation, indicators, wallet, prices) signature. Mirrors v1 for result comparison once the v2 engine is built. Not yet ready for production use. |
| Skew Landscape | demos/demo_skew_landscape.py |
Visualizes how LSGA's 2D skew memory evolves over generations. Replaces qx.backtest with a fake noise-landscape backtest and animates the optimizer's population. |
| Baseline Comparison | demos/run_v1_baseline.py |
Baseline performance benchmark against a previous version of the strategy. |
Run any demo directly:
python demos/extinction_event.py
Or use dispatch for interactive mode:
import qtradex as qx
from extinction_event import ExtinctionEvent
bot = ExtinctionEvent()
data = qx.Data("binance", "BTC", "USDT", days=365)
qx.dispatch(bot, data)
Community Strategies
The QTradeX-AI-Agents repo collects curated trading strategies built on the SDK — 22 bots including EMA crossovers, multi-indicator confluence strategies, Renko-RSI hybrids, Fourier-filtered signals, and the I-Ching hexagram bot. Known-good strategies are noted in the README. Clone it and drop them into your workflow.